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SilverBzH

Lizeur

by SilverBzH
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation2/5

    The two tools have overlapping purposes: both read PDF documents and extract text content. While read_pdf returns full OCR metadata and read_pdf_text returns only markdown text, an agent might struggle to choose between them when only text is needed, as both could technically serve that purpose. The descriptions help clarify the difference, but the core functionality is very similar.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern with snake_case: read_pdf and read_pdf_text. The naming is clear and predictable, making it easy for agents to understand the action (read) and target (pdf or pdf_text). There are no deviations or mixed conventions in this small set.

    Tool Count2/5

    With only 2 tools, this server feels under-scoped for a PDF processing domain. While the tools cover reading and extracting text, there are obvious gaps like creating, editing, or converting PDFs. A typical PDF server would benefit from more operations, making this count too low for comprehensive functionality.

    Completeness2/5

    The tool surface is severely incomplete for PDF processing. It only includes reading operations (two variants of the same basic function) and lacks essential capabilities such as creating PDFs, merging/splitting files, converting formats, or editing content. This will likely cause agent failures when more complex tasks are required.

  • Average 3.8/5 across 2 of 2 tools scored. Lowest: 3.2/5.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context: it describes the return format ('complete OCRResponse as a dictionary'), specifies that it includes all pages and metadata like markdown content and bounding boxes. However, it lacks details on permissions, error handling, performance, or other operational traits that would be helpful for an agent.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately sized with three sentences that are front-loaded: the first sentence states the core purpose, and the following sentences add important details about the response. There's minimal waste, though it could be slightly more structured by explicitly separating purpose from output details.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's moderate complexity (reading PDFs with OCR), no annotations, no output schema, and low schema coverage, the description is partially complete. It covers the output format and scope well but misses parameter explanations and behavioral context like error cases or limitations. It's adequate as a minimum viable description but has clear gaps.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 1 parameter with 0% description coverage, so the description must compensate. It doesn't mention the 'absolute_path' parameter at all, failing to explain what it represents or provide any usage context. Since schema coverage is low, the description adds no value beyond what the schema provides, resulting in a baseline score.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Read a PDF document and return the complete OCRResponse as a dictionary.' It specifies the verb ('read'), resource ('PDF document'), and output format ('OCRResponse as a dictionary'). However, it doesn't explicitly differentiate from its sibling 'read_pdf_text' beyond mentioning the full OCR response, leaving some ambiguity about the distinction.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. It mentions returning 'the complete OCR response' and not just the first page, but doesn't explain when to choose this over 'read_pdf_text' or other potential tools. There are no explicit when/when-not instructions or named alternatives beyond the sibling tool.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the full burden. It discloses the tool's behavior by stating it reads PDFs and returns markdown text, but lacks details on error handling, performance, or limitations (e.g., file size, supported PDF formats). The description adds some value but doesn't fully cover behavioral traits.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with the core purpose in the first sentence, followed by a concise explanation of its advantage over the sibling tool. Every sentence adds value without redundancy, making it efficient and well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has an output schema (which handles return values), no annotations, and low complexity, the description is mostly complete. It clearly states the purpose, usage guidelines, and output type. However, it could benefit from more behavioral details (e.g., error cases) to be fully comprehensive.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema description coverage is 0%, so the description must compensate. It doesn't explicitly mention the 'absolute_path' parameter, but implies it by referring to reading 'a PDF document.' Since there's only one parameter, the baseline is 4, as the description provides enough context to infer the parameter's purpose without detailed semantics.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb ('Read') and resource ('a PDF document'), specifies the output ('markdown text content from all pages'), and explicitly distinguishes it from its sibling tool ('read_pdf') by noting it's a simpler alternative that returns just text without full OCR metadata. This provides specific differentiation and purpose.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly states when to use this tool vs. its alternative: 'This is a simpler alternative to read_pdf that returns just the text content without the full OCR metadata, which can be easier for agents to process.' It clearly defines the context for choosing this tool over its sibling.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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